/***************************************************************/* Single & Multi-Objective Real-Coded Genetic Algorithms Code *//* Author: Kumara Sastry                                       *//* Illinois Genetic Algorithms Laboratory (IlliGAL)            *//* Deparment of General Engineering                            *//* University of Illinois at Urbana-Champaign                  *//* 104 S. Mathews Ave, Urbana, IL 61801                        *//***************************************************************/#include "crossover.hpp"// All constructors first - pretty straightforward stuffOneTwoPointCrossover::OneTwoPointCrossover(int numPoints):noOfPoints(numPoints) {}UniformCrossover::UniformCrossover(void):genewiseProbability(0.5) {}UniformCrossover::UniformCrossover(double geneProb):genewiseProbability(geneProb) {}SimulatedBinaryCrossover::SimulatedBinaryCrossover(void):genewiseProbability(0.5) {}SimulatedBinaryCrossover::SimulatedBinaryCrossover(double geneProb):genewiseProbability(geneProb) {}/** Single and Two Point Crossover: Single Point: Randomly select a crossover point and exchange the genes to the left of the crossover point to create two new individuals.Two Point: Randomly select two crossover points and exchange genes between the two crossover points to creat two new indivduals.*/void OneTwoPointCrossover::crossover(Individual *parent1, Individual *parent2){  int ii, XoverPt1, XoverPt2;  double Temp;  Individual child1(parent1), child2(parent2);  switch(noOfPoints) {  case 1:    XoverPt1 = myRandom.boundedIntegerRandom(0,globalSetup->noOfDecisionVariables-1);    for(ii = 0; ii <= XoverPt1; ii++) {      Temp = child1[ii];      child1.setValue(ii, child2[ii]);      child2.setValue(ii, Temp);    }    break;  case 2:    XoverPt1 = myRandom.boundedIntegerRandom(0,globalSetup->noOfDecisionVariables-1);    do {       XoverPt2 = myRandom.boundedIntegerRandom(0,globalSetup->noOfDecisionVariables-1);    }while(XoverPt1 == XoverPt2);    if(XoverPt1 > XoverPt2) SWAP(XoverPt1, XoverPt2);    for(ii = XoverPt1; ii < XoverPt2; ii++) {      Temp = child1[ii];      child1.setValue(ii, child2[ii]);      child2.setValue(ii, Temp);    }    break;  default: exit(0);  }    if(globalSetup->nichingType == DeterministicCrowding) {    child1.evaluateFitness();    child2.evaluateFitness();    deterministicCrowding(parent1, parent2, &child1, &child2);  }  else {    for(ii = 0; ii < globalSetup->noOfDecisionVariables; ii++) {      parent1->setValue(ii, child1[ii]);      parent2->setValue(ii, child2[ii]);    }  }}/// Uniform crossover: swap each gene with a probability of genewiseProbability (default value of 0.5)void UniformCrossover::crossover(Individual *parent1, Individual *parent2){  int ii;  double Temp;  Individual child1(parent1), child2(parent2);     if(myRandom.flip(globalSetup->xOverProbability)) {    for(ii = 0; ii < globalSetup->noOfDecisionVariables; ii++) {      if(myRandom.flip(genewiseProbability)) {	Temp = child1[ii];	child1.setValue(ii, child2[ii]);	child2.setValue(ii, Temp);      }    }    if(globalSetup->nichingType == DeterministicCrowding) {      child1.evaluateFitness();      child2.evaluateFitness();      deterministicCrowding(parent1, parent2, &child1, &child2);    }    else {      for(ii = 0; ii < globalSetup->noOfDecisionVariables; ii++) {	parent1->setValue(ii, child1[ii]);	parent2->setValue(ii, child2[ii]);      }    }  }}/**Simulated Binary Crossover: Creates new individuals based on a probability distribution (a polynomial one) similar to binary coded single point crossover. Reference: Deb, K., & Agarwal, R.B. (1995) "Simulated Binary Crossover for Continuous Search Space", Complex Systems. 9. pp. 115-148. (TCGA No. 05896).           Deb, K., & Kumar, A. (1995) "Real-Coded Genetic Algorithms with Simulated Binary Crossover: Studies on Multimodal and Multiobjective Problems", Complex Systems. 9. pp. 431-454. (TCGA No. 05897).*/void SimulatedBinaryCrossover::crossover(Individual *parent1, Individual *parent2){  int ii;  double gene1Value, gene2Value, beta, betaq, geneMin, geneMax, alpha;  double tempGeneValue;  double etaC; //The order of the polynomial for the SBX crossover  Individual child1(parent1), child2(parent2);  if(globalSetup->xOverParameters==NULL)    etaC = 10;  else    etaC = ((double *)globalSetup->xOverParameters)[1];  if(myRandom.flip(globalSetup->xOverProbability)) {    for(ii = 0; ii < globalSetup->noOfDecisionVariables; ii++) {      if((myRandom.flip(genewiseProbability)) && (fabs(child1[ii]-child2[ii]) >= ZERO)) {	geneMin = globalSetup->variableRanges[ii][0];	geneMax = globalSetup->variableRanges[ii][1];	if(child2[ii] > child1[ii]) { gene1Value = child1[ii]; gene2Value = child2[ii];}	else {gene1Value = child2[ii]; gene2Value = child1[ii];}	if((gene1Value - geneMin) > (geneMax - gene2Value)) 	  beta = (gene2Value-gene1Value)/(2.0*geneMax - gene2Value - gene1Value);	else 	  beta = (gene2Value-gene1Value)/(gene2Value + gene1Value - 2.0*geneMin);	alpha = myRandom.random01()*(2.0 - pow(beta,(double)(etaC+1)));	if(alpha <= 1.0)  betaq = pow(alpha, 1.0/((double)(etaC+1)));	else betaq = pow(1.0/(2.0-alpha), 1.0/((double)(etaC+1)));	tempGeneValue = 0.5*((gene1Value+gene2Value) - betaq*(gene2Value-gene1Value));	if(globalSetup->variableTypes[ii] == Integer) tempGeneValue = int(tempGeneValue+0.5);	child1.setValue(ii,tempGeneValue);	tempGeneValue = 0.5*((gene1Value+gene2Value) + betaq*(gene2Value-gene1Value));	if(globalSetup->variableTypes[ii] == Integer) tempGeneValue = int(tempGeneValue+0.5);
	child2.setValue(ii,tempGeneValue);      }    }    if(globalSetup->nichingType == DeterministicCrowding) {      child1.evaluateFitness();      child2.evaluateFitness();      deterministicCrowding(parent1, parent2, &child1, &child2);    }    else {      for(ii = 0; ii < globalSetup->noOfDecisionVariables; ii++) {	parent1->setValue(ii, child1[ii]);	parent2->setValue(ii, child2[ii]);      }    }  }}/**Deterministic Crowding: Compare each children obtained through crossover with the nearest parent based on phenotypic distance and replace the child with the nearest parent if the parent has higher fitnessReference: Mahfoud, S.W. (1992) "Crowding and Preselection Revisited", In R. Manner and B. Manderic (Eds.) Parallel Problem Solving From Nature, 2. (pp. 27-36). (IlliGAL Report No. 92004). http://www-illigal.ge.uiuc.edu/techreps.php3. (ftp://ftp-illigal.ge.uiuc.edu/pub/papers/IlliGALs/92004.ps.Z).*/void Crossover::deterministicCrowding(Individual *parent1, Individual *parent2, Individual *child1, Individual *child2) {  double phenotypeDist[4] = {0.0, 0.0, 0.0, 0.0};  int ii;  for(ii = 0; ii < globalSetup->noOfDecisionVariables; ii++) {    phenotypeDist[0] += (((*parent1)[ii]-(*child1)[ii])*((*parent1)[ii]-(*child1)[ii]));    phenotypeDist[1] += (((*parent1)[ii]-(*child2)[ii])*((*parent1)[ii]-(*child2)[ii]));    phenotypeDist[2] += (((*parent2)[ii]-(*child1)[ii])*((*parent2)[ii]-(*child1)[ii]));    phenotypeDist[3] += (((*parent2)[ii]-(*child2)[ii])*((*parent2)[ii]-(*child2)[ii]));  }    if (phenotypeDist[0]+phenotypeDist[3] <= phenotypeDist[1]+phenotypeDist[2]) {    if(isBetter(child1, parent1))       for(ii = 0; ii < globalSetup->noOfDecisionVariables; ii++) 	parent1->setValue(ii, (*child1)[ii]);    if(isBetter(child2, parent2))      for(ii = 0; ii < globalSetup->noOfDecisionVariables; ii++) 	parent2->setValue(ii, (*child2)[ii]);  }  else {    if(isBetter(child2, parent1))      for(ii = 0; ii < globalSetup->noOfDecisionVariables; ii++) 	parent1->setValue(ii, (*child2)[ii]);    if(isBetter(child1, parent2))      for(ii = 0; ii < globalSetup->noOfDecisionVariables; ii++) 	parent2->setValue(ii, (*child1)[ii]);  }}